

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    BertTokenizerFast,
	    BlipImageProcessor,
	    GPTaTokenizer,
	    InstructBlipProcessor,
	    PreTrainedTokenizerFast,
	)



@require_vision
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						List[Any] 							= tempfile.mkdtemp()

									a_						:						Any 							= BlipImageProcessor()
									a_						:						Optional[int] 							= GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""    )
									a_						:						Tuple 							= BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )

									a_						:						Dict 							= InstructBlipProcessor(_lowercase  ,      _lowercase  ,      _lowercase    )

									processor.save_pretrained(self.tmpdirname    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Tuple:
									return AutoProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    ).tokenizer





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> List[Any]:
									return AutoProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    ).image_processor





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Optional[int]:
									return AutoProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    ).qformer_tokenizer





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									shutil.rmtree(self.tmpdirname    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= [np.random.randint(255  ,      size=(3, 30, 400)  ,      dtype=np.uinta    )]

									a_						:						Optional[Any] 							= [Image.fromarray(np.moveaxis(_lowercase  ,      0  ,      -1    )    ) for x in image_inputs]

									return image_inputs





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						List[str] 							= InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()  ,      image_processor=self.get_image_processor()  ,      qformer_tokenizer=self.get_qformer_tokenizer()  ,      )
									processor.save_pretrained(self.tmpdirname    )

									a_						:						List[Any] 							= self.get_tokenizer(bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""    )
									a_						:						str 							= self.get_image_processor(do_normalize=_lowercase  ,      padding_value=1.0    )

									a_						:						List[str] 							= InstructBlipProcessor.from_pretrained(
									    self.tmpdirname  ,      bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""  ,      do_normalize=_lowercase  ,      padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer  ,      _lowercase    )

									self.assertEqual(processor.image_processor.to_json_string()  ,      image_processor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.image_processor  ,      _lowercase    )
									self.assertIsInstance(processor.qformer_tokenizer  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Optional[int] 							= self.get_image_processor()
									a_						:						str 							= self.get_tokenizer()
									a_						:						Dict 							= self.get_qformer_tokenizer()

									a_						:						List[str] 							= InstructBlipProcessor(
									    tokenizer=_lowercase  ,      image_processor=_lowercase  ,      qformer_tokenizer=_lowercase    )

									a_						:						int 							= self.prepare_image_inputs()

									a_						:						Any 							= image_processor(_lowercase  ,      return_tensors="""np"""    )
									a_						:						str 							= processor(images=_lowercase  ,      return_tensors="""np"""    )

									for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Any 							= self.get_image_processor()
									a_						:						Optional[int] 							= self.get_tokenizer()
									a_						:						str 							= self.get_qformer_tokenizer()

									a_						:						Tuple 							= InstructBlipProcessor(
									    tokenizer=_lowercase  ,      image_processor=_lowercase  ,      qformer_tokenizer=_lowercase    )

									a_						:						List[Any] 							= """lower newer"""

									a_						:						int 							= processor(text=_lowercase    )

									a_						:						Dict 							= tokenizer(_lowercase  ,      return_token_type_ids=_lowercase    )
									a_						:						Any 							= qformer_tokenizer(_lowercase  ,      return_token_type_ids=_lowercase    )

									for key in encoded_tokens.keys():
												self.assertListEqual(encoded_tokens[key]  ,      encoded_processor[key]    )

									for key in encoded_tokens_qformer.keys():
												self.assertListEqual(encoded_tokens_qformer[key]  ,      encoded_processor["""qformer_""" + key]    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.get_image_processor()
									a_						:						str 							= self.get_tokenizer()
									a_						:						Dict 							= self.get_qformer_tokenizer()

									a_						:						int 							= InstructBlipProcessor(
									    tokenizer=_lowercase  ,      image_processor=_lowercase  ,      qformer_tokenizer=_lowercase    )

									a_						:						Dict 							= """lower newer"""
									a_						:						Optional[Any] 							= self.prepare_image_inputs()

									a_						:						str 							= processor(text=_lowercase  ,      images=_lowercase    )

									self.assertListEqual(
									    list(inputs.keys()    )  ,      ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]  ,      )

									# test if it raises when no input is passed
									with pytest.raises(_lowercase    ):
												processor()





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						int 							= self.get_image_processor()
									a_						:						int 							= self.get_tokenizer()
									a_						:						Tuple 							= self.get_qformer_tokenizer()

									a_						:						Dict 							= InstructBlipProcessor(
									    tokenizer=_lowercase  ,      image_processor=_lowercase  ,      qformer_tokenizer=_lowercase    )

									a_						:						Optional[int] 							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_						:						int 							= processor.batch_decode(_lowercase    )
									a_						:						Any 							= tokenizer.batch_decode(_lowercase    )

									self.assertListEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						str 							= self.get_image_processor()
									a_						:						Dict 							= self.get_tokenizer()
									a_						:						Optional[int] 							= self.get_qformer_tokenizer()

									a_						:						Optional[int] 							= InstructBlipProcessor(
									    tokenizer=_lowercase  ,      image_processor=_lowercase  ,      qformer_tokenizer=_lowercase    )

									a_						:						List[str] 							= """lower newer"""
									a_						:						Union[str, Any] 							= self.prepare_image_inputs()

									a_						:						Union[str, Any] 							= processor(text=_lowercase  ,      images=_lowercase    )

									self.assertListEqual(
									    list(inputs.keys()    )  ,      ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]  ,      )





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


